Global losses to payment fraud have surged to $48 billion USD in 2023, and payment companies around the world are under more pressure to leverage new and existing technology to tackle this problem. Experts at leading chargeback technology platform Chargebacks911 report that AI is one of the key drivers to combat emerging payment threats and prevent disputed transactions.

When a customer disputes a debit or credit card transaction, the card issuer must determine whether to provide that cardholder with a refund for the transaction amount—also known as a chargeback. This dispute process is an act of parliament entitled to cardholders and is designed as a safety net to help consumers in cases of fraud or unfair merchant practices. However, while the protection is a necessary protection mechanism for consumers, the fragmented system is having a devastating effect on businesses by cardholders through a process known as “friendly fraud.”

Friendly fraud occurs when a cardholder wrongfully disputes a purchase with their issuing bank—either in an attempt to defraud a merchant or because of a misunderstanding. This can cause irreparable financial and reputational damage to retailers, costing them nearly four and a half times the amount of the reversed transaction, according to LexisNexis. 

“I think everyone agrees with the principle behind the chargeback mechanism. It is essential as an industry that we protect consumers but as consumers increasingly champion convenience, banks are more relied upon to resolve disputes through chargebacks, which are growing at an alarming rate and devastating merchants,” said Roger Alexander, a key advisor at Chargebacks911. “Chargebacks are not only capable of damaging a business financially, but they can damage your relationship with customers and payment processors—an impact that permeates the entire business—resulting in higher processing fees and jeopardising the future of a company.”

Friendly fraud takes many forms, and providing the right evidence to dispel each instance can be tedious and labour-intensive without automated processes. For example, if a customer claims that a product they purchased was never delivered, a merchant will have to gather and submit transaction data like proof-of-delivery photos or recipient signatures from the delivery company, or authentication measures from their own systems or the card scheme. 

VISA estimates that up to 75% of all chargebacks are likely cases of fraud, showcasing why more must be done to sort the legitimate claims from those that constitute friendly fraud. In the past, chargeback abuse has been difficult to detect because there has been minimal transaction data readily available to merchants who are trying to counter illegitimate chargebacks. Traditional chargeback management processes require employees to manually sift through vast amounts of data, which costs businesses time and money while also increasing the risk or human error.

The major card schemes have responded with various tools to help merchants reduce the number of chargebacks without harming consumers’ rights. For example, VISA’s Order Insight works to prevent chargebacks before they are issued, giving merchants the option to offer a customer a refund rather than going through the chargeback dispute process. In the back-end, its Compelling Evidence system can establish guidelines intended to streamline the evidence requirements for disputes. Other card schemes deploy similar technology, such as Mastercard’s Consumer Clarity and Mastercom Collaboration—a real-time data sharing platform similar to Order Insight and a dispute resolution platform with data sharing capabilities, respectively.

According to Alexander, what had been lacking in these solutions was a way of bringing all the information together alongside a merchant’s own data to create a complete picture of every chargeback claim quickly and clearly, allowing the merchant to properly represent themselves and prevent a greater percentage of first-party fraud and misuse. 

Through this conundrum, Chargebacks911 and its automated aggregation of transaction data was born.

“This is exactly what our technology was built for, and our AI capabilities have long been our ‘secret weapon’ for years,” said Alexander. “Since we first rolled out our platform to banks and businesses, the predictive power and efficiency of machine learning has enabled our customers to significantly reduce the legwork around analysing, compiling, segmenting and submitting transaction date, which can drastically reduce incidences of illegitimate chargebacks for merchants, safeguarding their revenue and customer relationships.”

Chargebacks911 compiles key transaction data from various sources—like card networks, cardholder’s banks and their own systems— and presents it through a learning-based dashboard that leverages AI  to learn the true source of chargebacks and suggest optimizations for businesses to integrate that can shore up any lapses in procedure or policy. It can continuously be updated and evolved in step with first-party fraud.

Founded over a decade ago, Chargebacks911 was the first chargeback remediation specialist in the world to tackle the chargeback fraud problem. Today, the global fintech leader has more than 400 subject matter experts at its disposal, and supports 27 different industries in nearly 100 countries—working closely with some of the largest retailers in the world. It manages over 2.4 billion transactions every month in all currencies and benefits the entire value chain, delivering positive results for merchants, acquirers and issuers.